Predicting User Satisfaction and Recommendation Intentions: A Machine Learning Approach Using Psychophysiological and Self-Reported Data

Victoria Oluwakemi Okesipe (), Théophile Demazure (), Jasmine Labelle (), Chenyi Huang (), Sylvain Sénécal (), Marc Fredette (), Romain Pourchon (), Constantinos K. Coursaris (), Alexander J. Karran (), Shang Lin Chen () and Pierre-Majorique Léger ()
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Victoria Oluwakemi Okesipe: HEC Montréal
Théophile Demazure: HEC Montréal
Jasmine Labelle: HEC Montréal
Chenyi Huang: HEC Montréal
Sylvain Sénécal: HEC Montréal
Marc Fredette: HEC Montréal
Romain Pourchon: Deloitte Digital
Constantinos K. Coursaris: HEC Montréal
Alexander J. Karran: HEC Montréal
Shang Lin Chen: HEC Montréal
Pierre-Majorique Léger: HEC Montréal

A chapter in Information Systems and Neuroscience, 2025, pp 385-395 from Springer

Abstract: Abstract The finance sector, just like e-commerce, utilizes online platforms (websites or mobile apps) to deliver its services or products, making usability and user experience one of the key concerns of digital banking. Having identified a research gap in using psychophysiological data to understand the determinants of customer satisfaction on digital platforms, this study focuses on predicting factors influencing users’ satisfaction and intention to recommend a banking website using both self-reported and psychophysiological data. With a within-subject study design, we collected data on 100 participants. Our research-in-progress aims to develop a machine learning model capable of predicting real-time user satisfaction and the likelihood of a user recommending a digital banking experience to friends or colleagues. Results showed that psychophysiological metrics improved the prediction of users’ intention to recommend. Similar features such as Phasic EDA, pupil size, time-to-first-mouse-click, k-coefficient, emotional valence, and subjective success were found to be good predictors of both intention to recommend and customer satisfaction.

Keywords: Digital platforms; Customer satisfaction; E-commerce; Machine learning; Psychophysiological measures; User experience (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-71385-9_34

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